Overview

Dataset statistics

Number of variables16
Number of observations358
Missing cells1161
Missing cells (%)20.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.9 KiB
Average record size in memory128.4 B

Variable types

Numeric8
Categorical8

Warnings

URL has a high cardinality: 352 distinct values High cardinality
Product Name has a high cardinality: 352 distinct values High cardinality
Provider/Company Name has a high cardinality: 284 distinct values High cardinality
state is highly correlated with Country1 and 1 other fieldsHigh correlation
pct_black is highly correlated with hispanic and 1 other fieldsHigh correlation
hispanic is highly correlated with pct_black and 1 other fieldsHigh correlation
pct_free is highly correlated with pct_black and 2 other fieldsHigh correlation
reduced is highly correlated with pct_freeHigh correlation
Country1 is highly correlated with state and 1 other fieldsHigh correlation
Country2 is highly correlated with state and 1 other fieldsHigh correlation
local_exp is highly correlated with federal_expHigh correlation
federal_exp is highly correlated with local_expHigh correlation
state is highly correlated with Country1 and 1 other fieldsHigh correlation
pct_black is highly correlated with hispanic and 1 other fieldsHigh correlation
hispanic is highly correlated with pct_black and 1 other fieldsHigh correlation
pct_free is highly correlated with pct_black and 2 other fieldsHigh correlation
reduced is highly correlated with pct_freeHigh correlation
Country1 is highly correlated with state and 1 other fieldsHigh correlation
Country2 is highly correlated with state and 1 other fieldsHigh correlation
local_exp is highly correlated with federal_expHigh correlation
federal_exp is highly correlated with local_expHigh correlation
state is highly correlated with Country1 and 1 other fieldsHigh correlation
pct_black is highly correlated with hispanicHigh correlation
hispanic is highly correlated with pct_blackHigh correlation
pct_free is highly correlated with reducedHigh correlation
reduced is highly correlated with pct_freeHigh correlation
Country1 is highly correlated with state and 1 other fieldsHigh correlation
Country2 is highly correlated with state and 1 other fieldsHigh correlation
local_exp is highly correlated with federal_expHigh correlation
federal_exp is highly correlated with local_expHigh correlation
reduced is highly correlated with pct_free and 3 other fieldsHigh correlation
pct_free is highly correlated with reduced and 5 other fieldsHigh correlation
local_exp is highly correlated with pct_free and 2 other fieldsHigh correlation
hispanic is highly correlated with reduced and 5 other fieldsHigh correlation
Country1 is highly correlated with hispanic and 3 other fieldsHigh correlation
federal_exp is highly correlated with pct_free and 2 other fieldsHigh correlation
state is highly correlated with reduced and 7 other fieldsHigh correlation
Primary Essential Function is highly correlated with Sector(s)High correlation
Country2 is highly correlated with hispanic and 3 other fieldsHigh correlation
pct_black is highly correlated with reduced and 5 other fieldsHigh correlation
Sector(s) is highly correlated with Primary Essential FunctionHigh correlation
Country2 is highly correlated with Country1High correlation
Country1 is highly correlated with Country2High correlation
state has 125 (34.9%) missing values Missing
pct_black has 125 (34.9%) missing values Missing
hispanic has 125 (34.9%) missing values Missing
pct_free has 125 (34.9%) missing values Missing
reduced has 125 (34.9%) missing values Missing
Country1 has 125 (34.9%) missing values Missing
Country2 has 125 (34.9%) missing values Missing
local_exp has 125 (34.9%) missing values Missing
federal_exp has 125 (34.9%) missing values Missing
LP ID has 6 (1.7%) missing values Missing
URL has 6 (1.7%) missing values Missing
Product Name has 6 (1.7%) missing values Missing
Provider/Company Name has 6 (1.7%) missing values Missing
Sector(s) has 6 (1.7%) missing values Missing
Primary Essential Function has 6 (1.7%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
URL is uniformly distributed Uniform
Product Name is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
pct_black has 116 (32.4%) zeros Zeros
hispanic has 8 (2.2%) zeros Zeros
pct_free has 46 (12.8%) zeros Zeros

Reproduction

Analysis started2021-09-10 17:24:59.564061
Analysis finished2021-09-10 17:26:20.865617
Duration1 minute and 21.3 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct358
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.5502793
Minimum0
Maximum369
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:21.223594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.85
Q189.25
median178.5
Q3271.75
95-th percentile348.15
Maximum369
Range369
Interquartile range (IQR)182.5

Descriptive statistics

Standard deviation106.2403938
Coefficient of variation (CV)0.58842553
Kurtosis-1.17594003
Mean180.5502793
Median Absolute Deviation (MAD)91.5
Skewness0.05026851676
Sum64637
Variance11287.02127
MonotonicityStrictly increasing
2021-09-10T22:56:21.984547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.3%
2241
 
0.3%
2451
 
0.3%
2441
 
0.3%
2431
 
0.3%
2421
 
0.3%
2411
 
0.3%
2401
 
0.3%
2391
 
0.3%
2381
 
0.3%
Other values (348)348
97.2%
ValueCountFrequency (%)
01
0.3%
11
0.3%
21
0.3%
31
0.3%
41
0.3%
51
0.3%
61
0.3%
71
0.3%
81
0.3%
91
0.3%
ValueCountFrequency (%)
3691
0.3%
3681
0.3%
3671
0.3%
3661
0.3%
3651
0.3%
3641
0.3%
3631
0.3%
3621
0.3%
3611
0.3%
3601
0.3%

state
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct24
Distinct (%)10.3%
Missing125
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean13.09012876
Minimum0
Maximum23
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:22.456519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15
median14
Q322
95-th percentile23
Maximum23
Range23
Interquartile range (IQR)17

Descriptive statistics

Standard deviation8.315727542
Coefficient of variation (CV)0.6352670548
Kurtosis-1.61243877
Mean13.09012876
Median Absolute Deviation (MAD)9
Skewness-0.1284921321
Sum3050
Variance69.15132455
MonotonicityNot monotonic
2021-09-10T22:56:22.782497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2357
15.9%
230
 
8.4%
1929
 
8.1%
721
 
5.9%
518
 
5.0%
112
 
3.4%
1611
 
3.1%
138
 
2.2%
67
 
2.0%
216
 
1.7%
Other values (14)34
 
9.5%
(Missing)125
34.9%
ValueCountFrequency (%)
01
 
0.3%
112
 
3.4%
230
8.4%
33
 
0.8%
41
 
0.3%
518
5.0%
67
 
2.0%
721
5.9%
82
 
0.6%
91
 
0.3%
ValueCountFrequency (%)
2357
15.9%
223
 
0.8%
216
 
1.7%
204
 
1.1%
1929
8.1%
182
 
0.6%
172
 
0.6%
1611
 
3.1%
151
 
0.3%
144
 
1.1%

pct_black
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)2.6%
Missing125
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean0.2509484123
Minimum0
Maximum0.894427191
Zeros116
Zeros (%)32.4%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:23.179471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.3738619094
Q30.4472135955
95-th percentile0.7745966692
Maximum0.894427191
Range0.894427191
Interquartile range (IQR)0.4472135955

Descriptive statistics

Standard deviation0.2777204456
Coefficient of variation (CV)1.106683414
Kurtosis-0.7583520458
Mean0.2509484123
Median Absolute Deviation (MAD)0.3738619094
Skewness0.6310460872
Sum58.47098006
Variance0.07712864588
MonotonicityNot monotonic
2021-09-10T22:56:23.502448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0116
32.4%
0.373861909457
15.9%
0.447213595524
 
6.7%
0.63245553217
 
4.7%
0.774596669211
 
3.1%
0.8944271918
 
2.2%
(Missing)125
34.9%
ValueCountFrequency (%)
0116
32.4%
0.373861909457
15.9%
0.447213595524
 
6.7%
0.63245553217
 
4.7%
0.774596669211
 
3.1%
0.8944271918
 
2.2%
ValueCountFrequency (%)
0.8944271918
 
2.2%
0.774596669211
 
3.1%
0.63245553217
 
4.7%
0.447213595524
 
6.7%
0.373861909457
15.9%
0116
32.4%

hispanic
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)2.6%
Missing125
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean-1.207530709
Minimum-1.609437912
Maximum0
Zeros8
Zeros (%)2.2%
Negative225
Negative (%)62.8%
Memory size2.9 KiB
2021-09-10T22:56:23.768433image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.609437912
5-th percentile-1.609437912
Q1-1.609437912
median-1.079478334
Q3-0.9162907319
95-th percentile-0.2231435513
Maximum0
Range1.609437912
Interquartile range (IQR)0.6931471806

Descriptive statistics

Standard deviation0.4747220963
Coefficient of variation (CV)-0.3931345952
Kurtosis-0.02939453162
Mean-1.207530709
Median Absolute Deviation (MAD)0.5299595784
Skewness0.955394118
Sum-281.3546551
Variance0.2253610687
MonotonicityNot monotonic
2021-09-10T22:56:24.045464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
-1.609437912116
32.4%
-1.07947833457
15.9%
-0.916290731924
 
6.7%
-0.510825623817
 
4.7%
-0.223143551311
 
3.1%
08
 
2.2%
(Missing)125
34.9%
ValueCountFrequency (%)
-1.609437912116
32.4%
-1.07947833457
15.9%
-0.916290731924
 
6.7%
-0.510825623817
 
4.7%
-0.223143551311
 
3.1%
08
 
2.2%
ValueCountFrequency (%)
08
 
2.2%
-0.223143551311
 
3.1%
-0.510825623817
 
4.7%
-0.916290731924
 
6.7%
-1.07947833457
15.9%
-1.609437912116
32.4%

pct_free
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct6
Distinct (%)2.6%
Missing125
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean0.2391891892
Minimum0
Maximum0.8
Zeros46
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:24.431389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.2391891892
Q30.2391891892
95-th percentile0.6
Maximum0.8
Range0.8
Interquartile range (IQR)0.03918918919

Descriptive statistics

Standard deviation0.1688284007
Coefficient of variation (CV)0.7058362516
Kurtosis1.269676999
Mean0.2391891892
Median Absolute Deviation (MAD)0.03918918919
Skewness0.7568043179
Sum55.73108108
Variance0.02850302889
MonotonicityNot monotonic
2021-09-10T22:56:24.715373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.239189189285
23.7%
0.248
 
13.4%
046
 
12.8%
0.437
 
10.3%
0.613
 
3.6%
0.84
 
1.1%
(Missing)125
34.9%
ValueCountFrequency (%)
046
12.8%
0.248
13.4%
0.239189189285
23.7%
0.437
10.3%
0.613
 
3.6%
0.84
 
1.1%
ValueCountFrequency (%)
0.84
 
1.1%
0.613
 
3.6%
0.437
10.3%
0.239189189285
23.7%
0.248
13.4%
046
12.8%

reduced
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.7%
Missing125
Missing (%)34.9%
Memory size2.9 KiB
0.4391891891891895
85 
0.4979729729729736
54 
0.4
48 
0.3412162162162158
46 

Length

Max length18
Median length18
Mean length14.90987124
Min length3

Characters and Unicode

Total characters3474
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.3412162162162158
2nd row0.4391891891891895
3rd row0.4
4th row0.4391891891891895
5th row0.4391891891891895

Common Values

ValueCountFrequency (%)
0.439189189189189585
23.7%
0.497972972972973654
15.1%
0.448
 
13.4%
0.341216216216215846
 
12.8%
(Missing)125
34.9%

Length

2021-09-10T22:56:25.473323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T22:56:25.700312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.439189189189189585
36.5%
0.497972972972973654
23.2%
0.448
20.6%
0.341216216216215846
19.7%

Most occurring characters

ValueCountFrequency (%)
9695
20.0%
1570
16.4%
8386
11.1%
2346
10.0%
7270
 
7.8%
0233
 
6.7%
.233
 
6.7%
4233
 
6.7%
6192
 
5.5%
3185
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3241
93.3%
Other Punctuation233
 
6.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9695
21.4%
1570
17.6%
8386
11.9%
2346
10.7%
7270
 
8.3%
0233
 
7.2%
4233
 
7.2%
6192
 
5.9%
3185
 
5.7%
5131
 
4.0%
Other Punctuation
ValueCountFrequency (%)
.233
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3474
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9695
20.0%
1570
16.4%
8386
11.1%
2346
10.0%
7270
 
7.8%
0233
 
6.7%
.233
 
6.7%
4233
 
6.7%
6192
 
5.5%
3185
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII3474
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9695
20.0%
1570
16.4%
8386
11.1%
2346
10.0%
7270
 
7.8%
0233
 
6.7%
.233
 
6.7%
4233
 
6.7%
6192
 
5.5%
3185
 
5.3%

Country1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)1.3%
Missing125
Missing (%)34.9%
Memory size2.9 KiB
-58858165002496.63
161 
-33572868438855.062
71 
0.0
 
1

Length

Max length19
Median length18
Mean length18.24034335
Min length3

Characters and Unicode

Total characters4250
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row-58858165002496.63
2nd row-33572868438855.062
3rd row-58858165002496.63
4th row-33572868438855.062
5th row-33572868438855.062

Common Values

ValueCountFrequency (%)
-58858165002496.63161
45.0%
-33572868438855.06271
19.8%
0.01
 
0.3%
(Missing)125
34.9%

Length

2021-09-10T22:56:26.553255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T22:56:26.823239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
58858165002496.63161
69.1%
33572868438855.06271
30.5%
0.01
 
0.4%

Most occurring characters

ValueCountFrequency (%)
8767
18.0%
5696
16.4%
6625
14.7%
0395
9.3%
3374
8.8%
2303
 
7.1%
.233
 
5.5%
-232
 
5.5%
4232
 
5.5%
1161
 
3.8%
Other values (2)232
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3785
89.1%
Other Punctuation233
 
5.5%
Dash Punctuation232
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8767
20.3%
5696
18.4%
6625
16.5%
0395
10.4%
3374
9.9%
2303
 
8.0%
4232
 
6.1%
1161
 
4.3%
9161
 
4.3%
771
 
1.9%
Dash Punctuation
ValueCountFrequency (%)
-232
100.0%
Other Punctuation
ValueCountFrequency (%)
.233
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8767
18.0%
5696
16.4%
6625
14.7%
0395
9.3%
3374
8.8%
2303
 
7.1%
.233
 
5.5%
-232
 
5.5%
4232
 
5.5%
1161
 
3.8%
Other values (2)232
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII4250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8767
18.0%
5696
16.4%
6625
14.7%
0395
9.3%
3374
8.8%
2303
 
7.1%
.233
 
5.5%
-232
 
5.5%
4232
 
5.5%
1161
 
3.8%
Other values (2)232
 
5.5%

Country2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct3
Distinct (%)1.3%
Missing125
Missing (%)34.9%
Memory size2.9 KiB
0.0
161 
0.005100200071153
71 
0.0127505001778826
 
1

Length

Max length18
Median length3
Mean length7.330472103
Min length3

Characters and Unicode

Total characters1708
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row0.0
2nd row0.005100200071153
3rd row0.0
4th row0.005100200071153
5th row0.005100200071153

Common Values

ValueCountFrequency (%)
0.0161
45.0%
0.00510020007115371
19.8%
0.01275050017788261
 
0.3%
(Missing)125
34.9%

Length

2021-09-10T22:56:27.629190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T22:56:27.900171image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0161
69.1%
0.00510020007115371
30.5%
0.01275050017788261
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0895
52.4%
.233
 
13.6%
1215
 
12.6%
5144
 
8.4%
774
 
4.3%
273
 
4.3%
371
 
4.2%
82
 
0.1%
61
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1475
86.4%
Other Punctuation233
 
13.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0895
60.7%
1215
 
14.6%
5144
 
9.8%
774
 
5.0%
273
 
4.9%
371
 
4.8%
82
 
0.1%
61
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.233
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1708
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0895
52.4%
.233
 
13.6%
1215
 
12.6%
5144
 
8.4%
774
 
4.3%
273
 
4.3%
371
 
4.2%
82
 
0.1%
61
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1708
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0895
52.4%
.233
 
13.6%
1215
 
12.6%
5144
 
8.4%
774
 
4.3%
273
 
4.3%
371
 
4.2%
82
 
0.1%
61
 
0.1%

local_exp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.6%
Missing125
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean9.31015213
Minimum9.004017268
Maximum9.554212212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:28.128156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9.004017268
5-th percentile9.004017268
Q19.210340372
median9.347889108
Q39.347889108
95-th percentile9.554212212
Maximum9.554212212
Range0.5501949441
Interquartile range (IQR)0.137548736

Descriptive statistics

Standard deviation0.174215035
Coefficient of variation (CV)0.01871237253
Kurtosis-0.4598643941
Mean9.31015213
Median Absolute Deviation (MAD)0.04477282078
Skewness-0.5891949725
Sum2169.265446
Variance0.03035087841
MonotonicityNot monotonic
2021-09-10T22:56:28.497682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9.347889108115
32.1%
9.00401726845
 
12.6%
9.55421221226
 
7.3%
9.21034037217
 
4.7%
9.39266192915
 
4.2%
9.54681260915
 
4.2%
(Missing)125
34.9%
ValueCountFrequency (%)
9.00401726845
 
12.6%
9.21034037217
 
4.7%
9.347889108115
32.1%
9.39266192915
 
4.2%
9.54681260915
 
4.2%
9.55421221226
 
7.3%
ValueCountFrequency (%)
9.55421221226
 
7.3%
9.54681260915
 
4.2%
9.39266192915
 
4.2%
9.347889108115
32.1%
9.21034037217
 
4.7%
9.00401726845
 
12.6%

federal_exp
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct6
Distinct (%)2.6%
Missing125
Missing (%)34.9%
Infinite0
Infinite (%)0.0%
Mean9.477065442
Minimum9.218814897
Maximum9.682406981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:28.784711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9.218814897
5-th percentile9.218814897
Q19.392661929
median9.50855995
Q39.50855995
95-th percentile9.682406981
Maximum9.682406981
Range0.4635920838
Interquartile range (IQR)0.115898021

Descriptive statistics

Standard deviation0.1471785374
Coefficient of variation (CV)0.01552996951
Kurtosis-0.4625568076
Mean9.477065442
Median Absolute Deviation (MAD)0.03825265887
Skewness-0.580243153
Sum2208.156248
Variance0.02166152187
MonotonicityNot monotonic
2021-09-10T22:56:29.104689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
9.50855995115
32.1%
9.21881489745
 
12.6%
9.68240698126
 
7.3%
9.39266192917
 
4.7%
9.54681260915
 
4.2%
9.68034400115
 
4.2%
(Missing)125
34.9%
ValueCountFrequency (%)
9.21881489745
 
12.6%
9.39266192917
 
4.7%
9.50855995115
32.1%
9.54681260915
 
4.2%
9.68034400115
 
4.2%
9.68240698126
 
7.3%
ValueCountFrequency (%)
9.68240698126
 
7.3%
9.68034400115
 
4.2%
9.54681260915
 
4.2%
9.50855995115
32.1%
9.39266192917
 
4.7%
9.21881489745
 
12.6%

LP ID
Real number (ℝ≥0)

MISSING

Distinct352
Distinct (%)100.0%
Missing6
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean54250.03977
Minimum10533
Maximum99916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2021-09-10T22:56:29.576659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10533
5-th percentile13965.9
Q129802
median53434
Q376996.75
95-th percentile95808.4
Maximum99916
Range89383
Interquartile range (IQR)47194.75

Descriptive statistics

Standard deviation26431.3227
Coefficient of variation (CV)0.4872129644
Kurtosis-1.219276743
Mean54250.03977
Median Absolute Deviation (MAD)23636
Skewness0.03762977846
Sum19096014
Variance698614819.4
MonotonicityNot monotonic
2021-09-10T22:56:29.982331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
506261
 
0.3%
614411
 
0.3%
128031
 
0.3%
831181
 
0.3%
936901
 
0.3%
368591
 
0.3%
114061
 
0.3%
501681
 
0.3%
206771
 
0.3%
731041
 
0.3%
Other values (342)342
95.5%
(Missing)6
 
1.7%
ValueCountFrequency (%)
105331
0.3%
106501
0.3%
107451
0.3%
110691
0.3%
112061
0.3%
112371
0.3%
112861
0.3%
113131
0.3%
114061
0.3%
115851
0.3%
ValueCountFrequency (%)
999161
0.3%
997891
0.3%
995801
0.3%
988451
0.3%
984681
0.3%
984021
0.3%
982651
0.3%
981281
0.3%
981021
0.3%
980011
0.3%

URL
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct352
Distinct (%)100.0%
Missing6
Missing (%)1.7%
Memory size2.9 KiB
https://www.soundtrap.com/
 
1
http://www.quia.com/web
 
1
http://www.audible.com
 
1
https://bookcreator.com/
 
1
https://www.reflexmath.com/
 
1
Other values (347)
347 

Length

Max length99
Median length25
Mean length28.47443182
Min length14

Characters and Unicode

Total characters10023
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique352 ?
Unique (%)100.0%

Sample

1st rowhttps://www.splashmath.com
2nd rowhttps://abcmouse.com
3rd rowhttps://www.abcya.com
4th rowhttp://www.aleks.com/
5th rowhttps://www.achieve3000.com/

Common Values

ValueCountFrequency (%)
https://www.soundtrap.com/1
 
0.3%
http://www.quia.com/web1
 
0.3%
http://www.audible.com1
 
0.3%
https://bookcreator.com/1
 
0.3%
https://www.reflexmath.com/1
 
0.3%
http://www.readtheory.org/1
 
0.3%
https://www.curriculumassociates.com/products/i-ready1
 
0.3%
https://beinternetawesome.withgoogle.com/en_us/interland1
 
0.3%
https://minecraft.net/en-us/1
 
0.3%
http://www.youtube.com1
 
0.3%
Other values (342)342
95.5%
(Missing)6
 
1.7%

Length

2021-09-10T22:56:30.917509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://safeshare.tv1
 
0.3%
http://onlinelibrary.uen.org1
 
0.3%
http://www.audible.com1
 
0.3%
http://www.flocabulary.com1
 
0.3%
http://web.stmath.com1
 
0.3%
http://wistia.com1
 
0.3%
http://www.mheducation.com1
 
0.3%
https://canva.com1
 
0.3%
https://www.renaissance.com1
 
0.3%
https://www.safeschools.com1
 
0.3%
Other values (342)342
97.2%

Most occurring characters

ValueCountFrequency (%)
/1037
 
10.3%
t994
 
9.9%
w789
 
7.9%
o721
 
7.2%
.663
 
6.6%
c519
 
5.2%
e506
 
5.0%
p491
 
4.9%
h477
 
4.8%
s466
 
4.6%
Other values (52)3360
33.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7808
77.9%
Other Punctuation2072
 
20.7%
Dash Punctuation52
 
0.5%
Decimal Number51
 
0.5%
Uppercase Letter28
 
0.3%
Math Symbol7
 
0.1%
Connector Punctuation5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t994
12.7%
w789
 
10.1%
o721
 
9.2%
c519
 
6.6%
e506
 
6.5%
p491
 
6.3%
h477
 
6.1%
s466
 
6.0%
m445
 
5.7%
a357
 
4.6%
Other values (16)2043
26.2%
Uppercase Letter
ValueCountFrequency (%)
C3
10.7%
A3
10.7%
B3
10.7%
D2
 
7.1%
U2
 
7.1%
F2
 
7.1%
G2
 
7.1%
M2
 
7.1%
S1
 
3.6%
P1
 
3.6%
Other values (7)7
25.0%
Decimal Number
ValueCountFrequency (%)
013
25.5%
210
19.6%
47
13.7%
36
11.8%
55
 
9.8%
13
 
5.9%
63
 
5.9%
82
 
3.9%
72
 
3.9%
Other Punctuation
ValueCountFrequency (%)
/1037
50.0%
.663
32.0%
:352
 
17.0%
%8
 
0.4%
?7
 
0.3%
#4
 
0.2%
!1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-52
100.0%
Math Symbol
ValueCountFrequency (%)
=7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7836
78.2%
Common2187
 
21.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t994
12.7%
w789
 
10.1%
o721
 
9.2%
c519
 
6.6%
e506
 
6.5%
p491
 
6.3%
h477
 
6.1%
s466
 
5.9%
m445
 
5.7%
a357
 
4.6%
Other values (33)2071
26.4%
Common
ValueCountFrequency (%)
/1037
47.4%
.663
30.3%
:352
 
16.1%
-52
 
2.4%
013
 
0.6%
210
 
0.5%
%8
 
0.4%
47
 
0.3%
?7
 
0.3%
=7
 
0.3%
Other values (9)31
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII10023
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/1037
 
10.3%
t994
 
9.9%
w789
 
7.9%
o721
 
7.2%
.663
 
6.6%
c519
 
5.2%
e506
 
5.0%
p491
 
4.9%
h477
 
4.8%
s466
 
4.6%
Other values (52)3360
33.5%

Product Name
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct352
Distinct (%)100.0%
Missing6
Missing (%)1.7%
Memory size2.9 KiB
Blooket
 
1
Instructure
 
1
PBS
 
1
i-Ready
 
1
Music Theory
 
1
Other values (347)
347 

Length

Max length45
Median length11
Mean length12.20738636
Min length2

Characters and Unicode

Total characters4297
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique352 ?
Unique (%)100.0%

Sample

1st rowSplashLearn
2nd rowABCmouse.com
3rd rowABCya!
4th rowALEKS
5th rowAchieve3000

Common Values

ValueCountFrequency (%)
Blooket1
 
0.3%
Instructure1
 
0.3%
PBS1
 
0.3%
i-Ready1
 
0.3%
Music Theory1
 
0.3%
PBS Kids1
 
0.3%
Goodreads1
 
0.3%
Vimeo1
 
0.3%
Khan Academy1
 
0.3%
Think Central1
 
0.3%
Other values (342)342
95.5%
(Missing)6
 
1.7%

Length

2021-09-10T22:56:31.870589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
google21
 
3.6%
math11
 
1.9%
learning10
 
1.7%
8
 
1.4%
education7
 
1.2%
for7
 
1.2%
kids5
 
0.9%
microsoft4
 
0.7%
science4
 
0.7%
the4
 
0.7%
Other values (443)505
86.2%

Most occurring characters

ValueCountFrequency (%)
e403
 
9.4%
o345
 
8.0%
a278
 
6.5%
i252
 
5.9%
234
 
5.4%
r232
 
5.4%
n221
 
5.1%
t203
 
4.7%
s183
 
4.3%
l170
 
4.0%
Other values (61)1776
41.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3256
75.8%
Uppercase Letter739
 
17.2%
Space Separator234
 
5.4%
Other Punctuation37
 
0.9%
Dash Punctuation19
 
0.4%
Decimal Number8
 
0.2%
Math Symbol2
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S82
 
11.1%
C67
 
9.1%
M55
 
7.4%
T49
 
6.6%
P46
 
6.2%
G46
 
6.2%
L41
 
5.5%
A40
 
5.4%
E37
 
5.0%
W34
 
4.6%
Other values (16)242
32.7%
Lowercase Letter
ValueCountFrequency (%)
e403
12.4%
o345
10.6%
a278
 
8.5%
i252
 
7.7%
r232
 
7.1%
n221
 
6.8%
t203
 
6.2%
s183
 
5.6%
l170
 
5.2%
c147
 
4.5%
Other values (16)822
25.2%
Other Punctuation
ValueCountFrequency (%)
.22
59.5%
!5
 
13.5%
:4
 
10.8%
'2
 
5.4%
&2
 
5.4%
¡1
 
2.7%
,1
 
2.7%
Decimal Number
ValueCountFrequency (%)
03
37.5%
31
 
12.5%
11
 
12.5%
21
 
12.5%
51
 
12.5%
41
 
12.5%
Math Symbol
ValueCountFrequency (%)
~1
50.0%
+1
50.0%
Space Separator
ValueCountFrequency (%)
234
100.0%
Dash Punctuation
ValueCountFrequency (%)
-19
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3995
93.0%
Common302
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e403
 
10.1%
o345
 
8.6%
a278
 
7.0%
i252
 
6.3%
r232
 
5.8%
n221
 
5.5%
t203
 
5.1%
s183
 
4.6%
l170
 
4.3%
c147
 
3.7%
Other values (42)1561
39.1%
Common
ValueCountFrequency (%)
234
77.5%
.22
 
7.3%
-19
 
6.3%
!5
 
1.7%
:4
 
1.3%
03
 
1.0%
'2
 
0.7%
&2
 
0.7%
31
 
0.3%
11
 
0.3%
Other values (9)9
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4296
> 99.9%
Latin 1 Sup1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e403
 
9.4%
o345
 
8.0%
a278
 
6.5%
i252
 
5.9%
234
 
5.4%
r232
 
5.4%
n221
 
5.1%
t203
 
4.7%
s183
 
4.3%
l170
 
4.0%
Other values (60)1775
41.3%
Latin 1 Sup
ValueCountFrequency (%)
¡1
100.0%

Provider/Company Name
Categorical

HIGH CARDINALITY
MISSING

Distinct284
Distinct (%)80.7%
Missing6
Missing (%)1.7%
Memory size2.9 KiB
Google LLC
 
27
Houghton Mifflin Harcourt
 
5
Microsoft
 
4
Learning A-Z
 
4
McGraw-Hill PreK-12
 
3
Other values (279)
309 

Length

Max length55
Median length12
Mean length14.44886364
Min length3

Characters and Unicode

Total characters5086
Distinct characters66
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique257 ?
Unique (%)73.0%

Sample

1st rowStudyPad Inc.
2nd rowAge of Learning, Inc
3rd rowABCya.com, LLC
4th rowMcGraw-Hill PreK-12
5th rowAchieve3000

Common Values

ValueCountFrequency (%)
Google LLC27
 
7.5%
Houghton Mifflin Harcourt5
 
1.4%
Microsoft4
 
1.1%
Learning A-Z4
 
1.1%
McGraw-Hill PreK-123
 
0.8%
PBS3
 
0.8%
Savvas Learning Company | Formerly Pearson K12 Learning3
 
0.8%
Autodesk, Inc3
 
0.8%
Curriculum Associates3
 
0.8%
ExploreLearning, LLC3
 
0.8%
Other values (274)294
82.1%
(Missing)6
 
1.7%

Length

2021-09-10T22:56:32.631695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc58
 
8.2%
llc48
 
6.8%
learning31
 
4.4%
google27
 
3.8%
the11
 
1.6%
education10
 
1.4%
of9
 
1.3%
ltd6
 
0.9%
microsoft5
 
0.7%
mifflin5
 
0.7%
Other values (381)494
70.2%

Most occurring characters

ValueCountFrequency (%)
e394
 
7.7%
o386
 
7.6%
372
 
7.3%
n334
 
6.6%
a297
 
5.8%
i283
 
5.6%
r248
 
4.9%
t223
 
4.4%
c203
 
4.0%
l182
 
3.6%
Other values (56)2164
42.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3569
70.2%
Uppercase Letter998
 
19.6%
Space Separator372
 
7.3%
Other Punctuation99
 
1.9%
Decimal Number22
 
0.4%
Dash Punctuation17
 
0.3%
Open Punctuation3
 
0.1%
Close Punctuation3
 
0.1%
Math Symbol3
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L165
16.5%
C116
11.6%
I94
 
9.4%
S71
 
7.1%
M55
 
5.5%
G53
 
5.3%
T52
 
5.2%
A49
 
4.9%
E39
 
3.9%
P37
 
3.7%
Other values (16)267
26.8%
Lowercase Letter
ValueCountFrequency (%)
e394
11.0%
o386
10.8%
n334
 
9.4%
a297
 
8.3%
i283
 
7.9%
r248
 
6.9%
t223
 
6.2%
c203
 
5.7%
l182
 
5.1%
s171
 
4.8%
Other values (16)848
23.8%
Decimal Number
ValueCountFrequency (%)
19
40.9%
27
31.8%
04
18.2%
31
 
4.5%
41
 
4.5%
Other Punctuation
ValueCountFrequency (%)
.54
54.5%
,43
43.4%
!1
 
1.0%
&1
 
1.0%
Space Separator
ValueCountFrequency (%)
372
100.0%
Dash Punctuation
ValueCountFrequency (%)
-17
100.0%
Open Punctuation
ValueCountFrequency (%)
(3
100.0%
Close Punctuation
ValueCountFrequency (%)
)3
100.0%
Math Symbol
ValueCountFrequency (%)
|3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4567
89.8%
Common519
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e394
 
8.6%
o386
 
8.5%
n334
 
7.3%
a297
 
6.5%
i283
 
6.2%
r248
 
5.4%
t223
 
4.9%
c203
 
4.4%
l182
 
4.0%
s171
 
3.7%
Other values (42)1846
40.4%
Common
ValueCountFrequency (%)
372
71.7%
.54
 
10.4%
,43
 
8.3%
-17
 
3.3%
19
 
1.7%
27
 
1.3%
04
 
0.8%
(3
 
0.6%
)3
 
0.6%
|3
 
0.6%
Other values (4)4
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5086
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e394
 
7.7%
o386
 
7.6%
372
 
7.3%
n334
 
6.6%
a297
 
5.8%
i283
 
5.6%
r248
 
4.9%
t223
 
4.4%
c203
 
4.0%
l182
 
3.6%
Other values (56)2164
42.5%

Sector(s)
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.4%
Missing6
Missing (%)1.7%
Memory size2.9 KiB
PreK-12
170 
PreK-12; Higher Ed; Corporate
115 
PreK-12; Higher Ed
65 
Higher Ed; Corporate
 
1
Corporate
 
1

Length

Max length29
Median length18
Mean length16.26136364
Min length7

Characters and Unicode

Total characters5724
Distinct characters20
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st rowPreK-12
2nd rowPreK-12
3rd rowPreK-12
4th rowPreK-12; Higher Ed
5th rowPreK-12

Common Values

ValueCountFrequency (%)
PreK-12170
47.5%
PreK-12; Higher Ed; Corporate115
32.1%
PreK-12; Higher Ed65
 
18.2%
Higher Ed; Corporate1
 
0.3%
Corporate1
 
0.3%
(Missing)6
 
1.7%

Length

2021-09-10T22:56:33.783815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T22:56:34.007647image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
prek-12350
42.2%
ed181
21.8%
higher181
21.8%
corporate117
 
14.1%

Most occurring characters

ValueCountFrequency (%)
r765
13.4%
e648
11.3%
477
 
8.3%
P350
 
6.1%
K350
 
6.1%
-350
 
6.1%
1350
 
6.1%
2350
 
6.1%
;296
 
5.2%
o234
 
4.1%
Other values (10)1554
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2722
47.6%
Uppercase Letter1179
20.6%
Decimal Number700
 
12.2%
Space Separator477
 
8.3%
Dash Punctuation350
 
6.1%
Other Punctuation296
 
5.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r765
28.1%
e648
23.8%
o234
 
8.6%
i181
 
6.6%
g181
 
6.6%
h181
 
6.6%
d181
 
6.6%
p117
 
4.3%
a117
 
4.3%
t117
 
4.3%
Uppercase Letter
ValueCountFrequency (%)
P350
29.7%
K350
29.7%
H181
15.4%
E181
15.4%
C117
 
9.9%
Decimal Number
ValueCountFrequency (%)
1350
50.0%
2350
50.0%
Dash Punctuation
ValueCountFrequency (%)
-350
100.0%
Other Punctuation
ValueCountFrequency (%)
;296
100.0%
Space Separator
ValueCountFrequency (%)
477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3901
68.2%
Common1823
31.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
r765
19.6%
e648
16.6%
P350
9.0%
K350
9.0%
o234
 
6.0%
H181
 
4.6%
i181
 
4.6%
g181
 
4.6%
h181
 
4.6%
E181
 
4.6%
Other values (5)649
16.6%
Common
ValueCountFrequency (%)
477
26.2%
-350
19.2%
1350
19.2%
2350
19.2%
;296
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r765
13.4%
e648
11.3%
477
 
8.3%
P350
 
6.1%
K350
 
6.1%
-350
 
6.1%
1350
 
6.1%
2350
 
6.1%
;296
 
5.2%
o234
 
4.1%
Other values (10)1554
27.1%

Primary Essential Function
Categorical

HIGH CORRELATION
MISSING

Distinct35
Distinct (%)9.9%
Missing6
Missing (%)1.7%
Memory size2.9 KiB
LC - Digital Learning Platforms
74 
LC - Sites, Resources & Reference
47 
LC - Content Creation & Curation
36 
LC - Study Tools
25 
LC - Sites, Resources & Reference - Games & Simulations
18 
Other values (30)
152 

Length

Max length73
Median length32
Mean length37.91193182
Min length11

Characters and Unicode

Total characters13345
Distinct characters49
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.1%

Sample

1st rowLC - Digital Learning Platforms
2nd rowLC - Digital Learning Platforms
3rd rowLC - Sites, Resources & Reference - Games & Simulations
4th rowLC - Digital Learning Platforms
5th rowLC - Digital Learning Platforms

Common Values

ValueCountFrequency (%)
LC - Digital Learning Platforms74
20.7%
LC - Sites, Resources & Reference47
13.1%
LC - Content Creation & Curation36
10.1%
LC - Study Tools25
 
7.0%
LC - Sites, Resources & Reference - Games & Simulations18
 
5.0%
LC - Courseware & Textbooks18
 
5.0%
LC/CM/SDO - Other16
 
4.5%
LC - Sites, Resources & Reference - Digital Collection & Repository15
 
4.2%
CM - Classroom Engagement & Instruction - Classroom Management11
 
3.1%
LC - Sites, Resources & Reference - Streaming Services9
 
2.5%
Other values (25)83
23.2%

Length

2021-09-10T22:56:35.029957image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
727
31.3%
lc272
 
11.7%
resources112
 
4.8%
sites101
 
4.4%
reference97
 
4.2%
digital89
 
3.8%
learning86
 
3.7%
platforms74
 
3.2%
classroom42
 
1.8%
study38
 
1.6%
Other values (81)683
29.4%

Most occurring characters

ValueCountFrequency (%)
1969
14.8%
e1318
 
9.9%
s764
 
5.7%
n753
 
5.6%
t733
 
5.5%
o710
 
5.3%
r703
 
5.3%
i693
 
5.2%
a636
 
4.8%
C542
 
4.1%
Other values (39)4524
33.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8395
62.9%
Uppercase Letter2084
 
15.6%
Space Separator1969
 
14.8%
Dash Punctuation464
 
3.5%
Other Punctuation417
 
3.1%
Open Punctuation8
 
0.1%
Close Punctuation8
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1318
15.7%
s764
9.1%
n753
9.0%
t733
8.7%
o710
8.5%
r703
8.4%
i693
8.3%
a636
7.6%
l371
 
4.4%
c329
 
3.9%
Other values (14)1385
16.5%
Uppercase Letter
ValueCountFrequency (%)
C542
26.0%
L384
18.4%
S292
14.0%
R244
11.7%
D163
 
7.8%
P91
 
4.4%
M88
 
4.2%
T75
 
3.6%
O71
 
3.4%
E27
 
1.3%
Other values (8)107
 
5.1%
Other Punctuation
ValueCountFrequency (%)
&271
65.0%
,114
27.3%
/32
 
7.7%
Space Separator
ValueCountFrequency (%)
1969
100.0%
Dash Punctuation
ValueCountFrequency (%)
-464
100.0%
Open Punctuation
ValueCountFrequency (%)
(8
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin10479
78.5%
Common2866
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1318
12.6%
s764
 
7.3%
n753
 
7.2%
t733
 
7.0%
o710
 
6.8%
r703
 
6.7%
i693
 
6.6%
a636
 
6.1%
C542
 
5.2%
L384
 
3.7%
Other values (32)3243
30.9%
Common
ValueCountFrequency (%)
1969
68.7%
-464
 
16.2%
&271
 
9.5%
,114
 
4.0%
/32
 
1.1%
(8
 
0.3%
)8
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII13345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1969
14.8%
e1318
 
9.9%
s764
 
5.7%
n753
 
5.6%
t733
 
5.5%
o710
 
5.3%
r703
 
5.3%
i693
 
5.2%
a636
 
4.8%
C542
 
4.1%
Other values (39)4524
33.9%

Interactions

2021-09-10T22:55:50.801014image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:51.200077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:51.532819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:51.846095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:52.269531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:52.627297image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:52.923632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:53.236123image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:53.653615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:53.952213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:54.968477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:55.342827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:55.703868image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:56.063232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:56.505661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:56.970634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:57.401544image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:57.809450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:58.215689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:58.610436image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:59.004973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:59.356783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:59.687885image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:55:59.969099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:00.377340image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:00.809017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:01.138863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:01.503378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:01.891404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:02.399924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:02.790997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:03.150346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:03.599153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:03.983783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:04.373959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:04.748496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:05.175943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:05.648307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:06.041839image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:06.680522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:07.038545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:07.375479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:07.747454image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:08.081478image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:08.533403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:08.881380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:09.264356image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:09.601333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:09.888364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:10.194323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:10.514274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:10.858254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:11.249233image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:11.660202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:11.997181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:12.476151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:12.979120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:13.418091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:13.793097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:14.060100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:14.437026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:14.830013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:15.314971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T22:56:15.850937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-10T22:56:35.480855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-10T22:56:36.105865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-10T22:56:36.746452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-10T22:56:37.308934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-10T22:56:37.855840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-10T22:56:16.749890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-10T22:56:17.789862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-10T22:56:18.760751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-10T22:56:20.394648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Unnamed: 0statepct_blackhispanicpct_freereducedCountry1Country2local_expfederal_expLP IDURLProduct NameProvider/Company NameSector(s)Primary Essential Function
005.00.000000-1.6094380.0000000.341216-5.885817e+130.00009.5468139.68034413117.0https://www.splashmath.comSplashLearnStudyPad Inc.PreK-12LC - Digital Learning Platforms
1123.00.373862-1.0794780.2391890.439189-3.357287e+130.00519.3478899.50856066933.0https://abcmouse.comABCmouse.comAge of Learning, IncPreK-12LC - Digital Learning Platforms
2219.00.000000-1.6094380.2000000.400000-5.885817e+130.00009.0040179.21881550479.0https://www.abcya.comABCya!ABCya.com, LLCPreK-12LC - Sites, Resources & Reference - Games & Simulations
3323.00.373862-1.0794780.2391890.439189-3.357287e+130.00519.3478899.50856092993.0http://www.aleks.com/ALEKSMcGraw-Hill PreK-12PreK-12; Higher EdLC - Digital Learning Platforms
4423.00.373862-1.0794780.2391890.439189-3.357287e+130.00519.3478899.50856073104.0https://www.achieve3000.com/Achieve3000Achieve3000PreK-12LC - Digital Learning Platforms
5522.00.000000-1.6094380.0000000.341216-5.885817e+130.00009.2103409.39266237600.0http://www.activelylearn.com/Actively LearnActively LearnPreK-12LC - Digital Learning Platforms
6619.00.000000-1.6094380.4000000.497973-5.885817e+130.00009.0040179.21881518663.0http://www.adaptedmind.comAdaptedMindGloWorldPreK-12LC - Digital Learning Platforms
7714.00.447214-0.9162910.2000000.400000-5.885817e+130.00009.0040179.21881565131.0http://www.amplify.com/AmplifyAmplify Education, Inc.PreK-12LC - Courseware & Textbooks
8819.00.000000-1.6094380.2000000.400000-5.885817e+130.00009.0040179.21881526491.0http://www.answers.com/AnswersAnswersPreK-12; Higher EdLC - Study Tools - Q&A
9914.00.632456-0.5108260.6000000.497973-5.885817e+130.00009.0040179.21881556441.0http://www.audible.comAudibleAmazon.com, Inc.PreK-12; Higher Ed; CorporateLC - Sites, Resources & Reference - Streaming Services

Last rows

Unnamed: 0statepct_blackhispanicpct_freereducedCountry1Country2local_expfederal_expLP IDURLProduct NameProvider/Company NameSector(s)Primary Essential Function
348360NaNNaNNaNNaNNaNNaNNaNNaNNaN64581.0https://hulu.comHuluHulu, LLCPreK-12; Higher EdLC - Sites, Resources & Reference - Streaming Services
349361NaNNaNNaNNaNNaNNaNNaNNaNNaN83704.0http://www.investopedia.comInvestopediaIACPreK-12; Higher Ed; CorporateLC - Sites, Resources & Reference
350362NaNNaNNaNNaNNaNNaNNaNNaNNaN70706.0https://canvas.apps.chrome/Canvas for ChromeCanvas Talent, Inc.PreK-12; Higher Ed; CorporateLC - Content Creation & Curation
351363NaNNaNNaNNaNNaNNaNNaNNaNNaN13282.0https://www.schoolspecialty.com/School SpecialtySchool Specialty IncPreK-12LC - Sites, Resources & References - Learning Materials & Supplies
352364NaNNaNNaNNaNNaNNaNNaNNaNNaN24396.0https://www.mathsisfun.com/Math is FunMathsisfun.comPreK-12LC - Sites, Resources & Reference
353365NaNNaNNaNNaNNaNNaNNaNNaNNaN22241.0https://www.history.com/History.comA&E Television Networks, LLCPreK-12; Higher EdLC - Sites, Resources & Reference
354366NaNNaNNaNNaNNaNNaNNaNNaNNaN93376.0https://www.cultofpedagogy.com/Cult of PedagogyCult of PedagogyPreK-12CM - Teacher Resources - Professional Learning
355367NaNNaNNaNNaNNaNNaNNaNNaNNaN88065.0https://dochub.com/DocHubDocHubPreK-12; Higher Ed; CorporateSDO - Other
356368NaNNaNNaNNaNNaNNaNNaNNaNNaN37805.0http://google.com/slides/about/Google SlidesGoogle LLCPreK-12; Higher Ed; CorporateLC - Content Creation & Curation
357369NaNNaNNaNNaNNaNNaNNaNNaNNaN32555.0http://www.innersloth.com/gameAmongUs.phpAmong UsInnerSlothPreK-12; Higher EdLC - Sites, Resources & Reference - Games & Simulations